R/CVPlot.R

In MCbiclust: Massive correlating biclusters for gene expression data and associated methods

#' Make correlation vector plot
#' 
#' A function to visualise the differences between different found biclusters. 
#' Output is a matrix of plots. Each correlation vector is plotted against each 
#' other across the entire  measured gene set in the lower diagonal plots, and
#' a chosen gene set (e.g. mitochondrial) in the upper diagonal plots. The diagnal
#' plots themselves show the density plots of the entire measured and chosen
#' gene set. There are addition options to set the transparancy of the data
#' points and names of the correlation vectors.
#' 
#' @param cv.df A dataframe containing the correlation vectors of one or more patterns.
#' @param geneset.loc A gene set of interest (e.g. mitochondrial) to be plotted separately from rest of genes.
#' @param geneset.name Name of geneset (e.g. mitochondrial genes)
#' @param alpha1 Transparency level of non-gene set genes
#' @param alpha2 Transparency level of gene set genes
#' @param cnames Character vector containing names for the correlation vector
#' @return A plot of the correlation vectors
#' @example example_code/example_cvplot.R
#' @export

CVPlot <- function (cv.df, geneset.loc, geneset.name, alpha1 = 0.005, alpha2 = 0.1, 
          cnames = NULL) 
{
  if (is.character(geneset.name) == FALSE) {
    stop("geneset.name must be a single character value")
  }
  cv.num <- dim(cv.df)[2]
  if (length(cnames) != cv.num) {
    colnames(cv.df)[seq_len(cv.num)] <- paste(rep("CV", cv.num), 
                                              seq_len(cv.num), sep = "")
  }
  else {
    colnames(cv.df) <- cnames
  }
  mito_status <- rep(paste("Non", geneset.name), dim(cv.df)[1])
  mito_status[geneset.loc] <- geneset.name
  cv.df$Status <- mito_status
  status.loc <- (colnames(cv.df) == "Status")
  if (dim(cv.df)[2] == 2) {
    a1 <- colnames(cv.df)[1]
    a2 <- "Status"
    p <- ggplot(cv.df, aes_(x = as.name(a1), col = as.name(a2))) + 
      xlab(a1)
    return(p + geom_density())
  }
  custom_cv_plot <- ggpairs(cv.df[, !status.loc], upper = "blank", 
                            lower = "blank", title = "", axisLabels = "show")
  col_cv <- hue_pal(h = c(0, 360) + 15, c = 100, l = 65, h.start = 0, 
                    direction = 1)(2)
  H_plot_fun <- function(a, b) {
    a1 <- colnames(cv.df)[a]
    b1 <- colnames(cv.df)[b]
    p <- ggplot(cv.df[-geneset.loc, ], aes_(x = as.name(a1), 
                                            y = as.name(b1))) + xlab(a1) + ylab(b1)
    return(p + geom_point(size = 2, alpha = alpha1, col = col_cv[2]))
  }
  H_combn <- combn(cv.num, 2)
  H_plot_fun2 <- function(i) {
    p1 <- H_plot_fun(H_combn[1, i], H_combn[2, i])
    return(p1)
  }
  p_H_plots <- lapply(seq_len(dim(H_combn)[2]), H_plot_fun2)
  for (i in seq_len(dim(H_combn)[2])) {
    custom_cv_plot <- putPlot(custom_cv_plot, p_H_plots[[i]], 
                              H_combn[2, i], H_combn[1, i])
  }
  H_m_plot_fun <- function(a, b) {
    a1 <- colnames(cv.df)[a]
    b1 <- colnames(cv.df)[b]
    p <- ggplot(cv.df[geneset.loc, ], aes_(x = as.name(a1), 
                                           y = as.name(b1))) + xlab(a1) + ylab(b1)
    return(p + geom_point(size = 2, alpha = alpha2, col = col_cv[1]))
  }
  H_m_plot_fun2 <- function(i) {
    p1 <- H_m_plot_fun(H_combn[1, i], H_combn[2, i])
    return(p1)
  }
  p_mH_plots <- lapply(seq_len(dim(H_combn)[2]), H_m_plot_fun2)
  for (i in seq_len(dim(H_combn)[2])) {
    custom_cv_plot <- putPlot(custom_cv_plot, p_mH_plots[[i]], 
                              H_combn[1, i], H_combn[2, i])
  }
  H_d_plot_fun <- function(a, l1 = FALSE) {
    a1 <- colnames(cv.df)[a]
    a2 <- "Status"
    p <- ggplot(cv.df, aes_(x = as.name(a1), col = as.name(a2))) + 
      xlab(a1)
    if (l1 == FALSE) {
      return(p + geom_density() + theme(legend.position = "none"))
    }
    else {
      return(p + geom_density())
    }
  }
  for (i in seq_len(cv.num - 1)) {
    custom_cv_plot <- putPlot(custom_cv_plot, H_d_plot_fun(i), 
                              i, i)
  }
  custom_cv_plot <- putPlot(custom_cv_plot, H_d_plot_fun(cv.num, 
                                                         l1 = FALSE), cv.num, cv.num)
  return(custom_cv_plot)
}